\(\vec{\mathbf {b}}_d \in \mathbb {R}^{|V|}, U_d \in \mathbb {R}^{|V| \times d_s}\)  are parameters and \(h(\cdot )\)  represents the average of vectors \(\vec{\mathbf {d}}_{i},\vec{\mathbf {w}}_{t-k_s},..., \vec{\mathbf {w}}_{t+k_s}\)  taken from \(D \ \text{and} \ W_s\) . Hierarchical softmax is used for faster training [1]}. Finally, after training, \(D\)  learns the users' document vectors which represent their stylometric features.
